import pandas as pd
from mlxtend.preprocessing import TransactionEncoder
from mlxtend.frequent_patterns import apriori,association_rules
import matplotlib.pyplot as plt
import networkx as nx

df = pd.read_excel('D:\\关联规则挖掘\\关联规则挖掘\\餐厅数据.xlsx')
# print(df.head)
# 转换菜品数据格式
trsations = df['菜品'].str.split(',').to_list()

# 标准化数据
ts = TransactionEncoder()
te_ary = ts.fit(trsations).transform(trsations)
df_encoded = pd.DataFrame(te_ary, columns=ts.columns_)

# print(df_encoded.head)
# 使用apriori进行关联规则挖掘
frequent_itemsets = apriori(df_encoded, min_support=0.1, use_colnames=True)
frequent_itemsets.sort_values(by="support", ascending=False, inplace=True)

# 选择2项集查看
print(frequent_itemsets[frequent_itemsets.itemsets.apply(lambda x : len(x) == 2)])

rules = association_rules(frequent_itemsets, metric="confidence", min_threshold=0.1)
# 筛选有效规则
effective = rules[
    (rules['lift'] > 1) & (rules['confidence'] > 0.1)
].sort_values(by=['lift', 'confidence'], ascending=False)

#生成相应的模型
frequent_itemsets.to_pickle("frequent_itemsets.pkl")
effective.to_pickle("rule.pkl")